




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


snake_case_  :      Union[str, Any]		=		R"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"





@add_start_docstrings(_lowerCAmelCase	)
class 					snake_case_   (     _lowerCAmelCase	):



				'''simple docstring'''
				lowerCamelCase       =  '''rag'''
				lowerCamelCase       =  True





				def __init__(      self							:   Tuple      ,							__magic_name__							:   Any=None      ,							__magic_name__							:   Tuple=True      ,							__magic_name__							:   str=None      ,							__magic_name__							:   Tuple=None      ,							__magic_name__							:   Union[str, Any]=None      ,							__magic_name__							:   Any=None      ,							__magic_name__							:   Union[str, Any]=None      ,							__magic_name__							:   str=" / "      ,							__magic_name__							:   Union[str, Any]=" // "      ,							__magic_name__							:   str=5      ,							__magic_name__							:   str=300      ,							__magic_name__							:   List[Any]=768      ,							__magic_name__							:   Optional[Any]=8      ,							__magic_name__							:   List[str]="wiki_dpr"      ,							__magic_name__							:   Union[str, Any]="train"      ,							__magic_name__							:   Optional[Any]="compressed"      ,							__magic_name__							:   Optional[int]=None      ,							__magic_name__							:   int=None      ,							__magic_name__							:   Optional[int]=False      ,							__magic_name__							:   Dict=False      ,							__magic_name__							:   Tuple=0.0      ,							__magic_name__							:   Dict=True      ,							__magic_name__							:   Optional[int]=False      ,							__magic_name__							:   int=False      ,							__magic_name__							:   Any=False      ,							__magic_name__							:   Union[str, Any]=True      ,							__magic_name__							:   Tuple=None      ,							**__magic_name__							:   Union[str, Any]      ,							)							->			List[Any]:
											super().__init__(
											    bos_token_id=UpperCAmelCase_      ,							pad_token_id=UpperCAmelCase_      ,							eos_token_id=UpperCAmelCase_      ,							decoder_start_token_id=UpperCAmelCase_      ,							forced_eos_token_id=UpperCAmelCase_      ,							is_encoder_decoder=UpperCAmelCase_      ,							prefix=UpperCAmelCase_      ,							vocab_size=UpperCAmelCase_      ,							**UpperCAmelCase_      ,							)
											assert (
											    "question_encoder" in kwargs and "generator" in kwargs
											), "Config has to be initialized with question_encoder and generator config"
											lowerCamelCase_			:    Optional[Any]   						=      kwargs.pop("question_encoder"							)
											lowerCamelCase_			:    int   						=      question_encoder_config.pop("model_type"							)
											lowerCamelCase_			:    str   						=      kwargs.pop("generator"							)
											lowerCamelCase_			:    str   						=      decoder_config.pop("model_type"							)

											from ..auto.configuration_auto import AutoConfig

											lowerCamelCase_			:    Optional[Any]   						=      AutoConfig.for_model(UpperCAmelCase_      ,							**UpperCAmelCase_							)
											lowerCamelCase_			:    Optional[int]   						=      AutoConfig.for_model(UpperCAmelCase_      ,							**UpperCAmelCase_							)

											lowerCamelCase_			:    int   						=      reduce_loss
											lowerCamelCase_			:    Optional[Any]   						=      label_smoothing
											lowerCamelCase_			:    List[str]   						=      exclude_bos_score
											lowerCamelCase_			:    Any   						=      do_marginalize

											lowerCamelCase_			:    Optional[int]   						=      title_sep
											lowerCamelCase_			:    Any   						=      doc_sep
											lowerCamelCase_			:    Optional[int]   						=      n_docs
											lowerCamelCase_			:    Tuple   						=      max_combined_length

											lowerCamelCase_			:    Any   						=      dataset
											lowerCamelCase_			:    Dict   						=      dataset_split
											lowerCamelCase_			:    Union[str, Any]   						=      index_name

											lowerCamelCase_			:    List[Any]   						=      retrieval_vector_size
											lowerCamelCase_			:    Optional[int]   						=      retrieval_batch_size
											lowerCamelCase_			:    Dict   						=      passages_path
											lowerCamelCase_			:    Dict   						=      index_path
											lowerCamelCase_			:    Union[str, Any]   						=      use_dummy_dataset

											lowerCamelCase_			:    Tuple   						=      output_retrieved

											lowerCamelCase_			:    int   						=      do_deduplication

											lowerCamelCase_			:    int   						=      use_cache

											if self.forced_eos_token_id is None:
																		lowerCamelCase_			:    int   						=      getattr(self.generator      ,							"forced_eos_token_id"      ,							UpperCAmelCase_							)





				@classmethod
				def    __SCREAMING_SNAKE_CASE     (      cls							:   Union[str, Any]      ,							__magic_name__							:   PretrainedConfig      ,							__magic_name__							:   PretrainedConfig      ,							**__magic_name__							:   Optional[Any]							)							->			PretrainedConfig:
											return cls(question_encoder=question_encoder_config.to_dict()      ,							generator=generator_config.to_dict()      ,							**UpperCAmelCase_							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   str							)							->			List[Any]:
											lowerCamelCase_			:    Optional[Any]   						=      copy.deepcopy(self.__dict__							)
											lowerCamelCase_			:    Any   						=      self.question_encoder.to_dict()
											lowerCamelCase_			:    List[Any]   						=      self.generator.to_dict()
											lowerCamelCase_			:    Optional[Any]   						=      self.__class__.model_type
											return output



"""simple docstring"""


class 	__a	:






       def __init__(	self			:				Any							,   UpperCAmelCase_			:				List[str]							,   UpperCAmelCase_			:				Optional[Any]							,   UpperCAmelCase_			:				Optional[Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		name
              UpperCamelCase       			=		value
              UpperCamelCase       			=		weight






       def __repr__(	self			:				int      )->	Any:







              """simple docstring"""
              return f"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Union[str, Any]:







              """simple docstring"""
              return self.value






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	int:







              """simple docstring"""
              return self.name






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	List[Any]:







              """simple docstring"""
              return self.weight






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Union[str, Any]:







              """simple docstring"""
              return self.value / self.weight





def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Union[str, Any]:







       """simple docstring"""
       UpperCamelCase       			=		[]
       for i in range(len(UpperCAmelCase_					)					):
              menu.append(Things(name[i]		,   value[i]		,   weight[i]					)					)
       return menu



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Dict:







       """simple docstring"""
       UpperCamelCase       			=		sorted(UpperCAmelCase_		,   key=UpperCAmelCase_		,   reverse=UpperCAmelCase_					)
       UpperCamelCase       			=		[]
       UpperCamelCase			,							UpperCamelCase       			=		0.0, 0.0
       for i in range(len(UpperCAmelCase_					)					):
              if (total_cost + items_copy[i].get_weight()) <= max_cost:
                     result.append(items_copy[i]					)
                     total_cost += items_copy[i].get_weight()
                     total_value += items_copy[i].get_value()
       return (result, total_value)



def 						lowerCamelCase__      (				)->				Dict:







       """simple docstring"""


if __name__ == "__main__":
      import doctest

      doctest.testmod()





